Water Power Technologies Office Wave Hindcast Dataset

WPTO Wave Hindcast Dataset is the default data available on the Marine Energy Atlas, an interactive mapping tool to explore potential for marine energy resources.

The atlas allows users to:

• Visualize or preview data before downloading
• Subset data easily to an area of interest or a particular year for download
• Download data for entire regions or multiple years using the large-scale data option on the Data Downloader
• Leverage in-app processing through the Capacity Factor Tool: estimate the capacity factor (the ratio of time-averaged power generation to the maximum power generation), using wave height from the WPTO Wave Hindcast Dataset and user-uploaded power matrices of wave energy converters.

Query Tools

To spatially subset data for download from the Marine Energy Atlas to your local machine, use the Query Tools. The Query Tools allows one to select data for discrete locations or larger areas. To retrieve information on a small, discrete area, one can input coordinates or click a point on the map. To obtain data on larger areas, one can use the region query, custom shape query, or the radius query and select an area of interest by double clicking and dragging the polygon on the map. For the queries where users delineate spatial extent on the map, once a polygon is set, the area of the polygon and the coordinates of the polygon corners will appear. Similarly, for data within a selected radius, the area of the circle, the radius of the circle, and the coordinate of the center will appear. Users will then have the option to Request Query Data or download the data. Selecting “Request Query Data” generates a table in the window to browse the data. Clicking on the download button will take the user to the Data Downloader with most of the fields pre-filled.

Data Downloader

Proceeding directly to the Data Downloader allows users to select multiple attributes. The data from the WPTO Wave Hindcast Dataset are only accessible through the “Large-Scale Data” download type. Users select the wave model of choice and then again spatially subset the data using the query tools. Once the area of interest has been selected, the number of sites within the polygon pops up. If no sites are found within the selected polygon, one should expand the polygon to capture a larger area. In the next step on the attributes page, users can select which wave data variables from the hindcast they would like to download, select a particular year in the dataset, and easily change the format of the data from UTC to local time. However, users are limited by the amount of data they can download in one instance, which is indicated by the download limit bar on the bottom of the page. Through the Marine Energy Atlas, users are only able to download data for a few years for one variable, or multiple variables for one year. We encourage users to use one of the other data download methods for downloading more data or the complete dataset.

You can also watch an NREL-hosted webinar New Functionality and WPTO Wave Hindcast Data in the Marine Energy Atlas, which provides an overview on the Marine Energy Atlas, how to access the data, and how to use the in-app processing tools.

The Marine and Hydrokinetic Toolkit (MHKiT) is a massive, searchable, open-source knowledge hub that provides marine energy developers with the code needed to analyze how well their technology might perform in various ocean and river sites. MHKiT is divided into various modules depending on the resource type and enables simple access to several marine data sources. Among these is the Wave Module, which includes tools to access the WPTO Wave Hindcast Dataset.

In addition to the WPTO Wave Hindcast Dataset, the Wave Module includes convenience functions to access the data and tools to calculate quantities of interest and visualize data for wave energy converters. MHKiT software enables simple access to the several marine data sources to compliment the WPTO Wave Hindcast Dataset and can be used to create or be incorporated into processing workflows.

A detailed overview of how to access data from the WPTO Wave Hindcast Dataset within MHKiT can be found on the software’s documentation webpage; API documentation for the WPTO Wave Hindcast Dataset in Python and for MATLAB. For access, use the example code for data through Python and the example code using MATLAB.

The data can also be accessed or downloaded using tools within the AWS ecosystem.

Download the Entire Dataset

If you have downloaded the AWS command line interface, the WPTO Wave Hindcast Dataset can easily be accessed through the AWS registry of open data. By copying a line of code into the terminal on your local machine, you can download the entire dataset. The data, arranged by year, currently occupy approximately 100 TB and are expected to grow to 200 TB.

Download a Subset of the Dataset

Users that want to download data for certain years from a particular region can use the web browser interface from the registry of open data. On the right panel, by clicking Browse Dataset under Explore and through Object v1.0.0, users can choose the region of interest and then download the files from their browser. These files contain all variables from the hindcast, and each file is between 81.9 GB and 455.2 GB for the spatial data and 2.5 GB and 3.6 GB for the virtual buoy data.

Users can also select data of interest through the user-friendly application programming interface through NREL’s Developer Network. However, this is highly rate-limited due to the complexity of the data. Users are limited by the amount of requests they can make within a 24-hour period.

Accessing the Data Within the AWS Ecosystem

Users can also access the data directly within the AWS ecosystem by either setting up a highly scalable data service (also known as HSDS) server or by reading the .h5 files directly. This approach has the advantage of not needing to download the data, but it does require require owning and managing your own AWS computational resources.

These variables are important across all sectors of wave energy. Anyone discussing wave energy opportunities will likely encounter these common variables:

• Wave height/significant wave height (H_s, meters [m])
  
  • The average height of the largest 33% of all waves in a series
  • Statistical calculation using spectral significant wave height
  • Used to design loads for normal operation (IEC 62600-2)
  • Reference: IEC 62600-100, -1, -2, -3.
• Wave period (T_m, seconds [s])
  • Time between wave crests (1/f)
  • The time that it takes for one complete wave, from crest to crest, to pass a fixed point
  • Reference: IEC 62600-101.
• Water depth (h, m)
• Depth from ocean surface to ocean floor 
• Measured (using multibeam echosounders)
• Depth is a design constraint for wave energy converters (WECs) and can be used to characterize project costs.
• Wave direction (q, degrees)
• Direction of individual wave energy flux (e.g., direction of individual wave energy flux, which is the rate of change of energy density in a wave)
• Statistical calculation from wave spectrum
• Reference: IEC 62600-101.
• Omnidirectional wave power (J, kilowatts per meter [kW/m])
  • Total energy flux arriving from all directions
  • “Time-averaged energy flux through an envisioned vertical cylinder of unit diameter, integrated from the seafloor to the surface” (see Equations 9 and 10 in IEC TS 62600-101)
  • Summary metric of theoretical energy resource that includes all wave energy at a site as the sum over all frequency and direction bins. Characterizes opportunity for wave power generation.
  • Reference: IEC 62600-100 and -101.

For project feasibility studies and technology designs, there are variables that can be used for more detailed analysis.

• Wave period (T_m)
  • Energy period (T_e, s)
    • The period corresponding to the weighted average of the wave energy
    • Variance-weighted mean period of the one-dimensional period variance density spectrum (derived in Equation 13 in IEC TS 62600-101)
    • For a specific wave spectrum, it is often calculated instead using the specific peak period where T_e = a * T_p; a represents the sea state and ranges from 0.86 to 1
    • Reference: IEC 62600-100 and -101.
  • Mean absolute period (T_p, s)
    • The mean of all wave periods in a time series of a certain sea state
    • Statistical calculation form wave spectrum
    • Can be used to target resonance bandwidth for WEC designs 
    • Reference: IEC TS 62600-101.
  • Peak period (T_p, s)
    • Period associated with the maximum energy value of the wave spectrum
    • Extracted from the wave spectra, calculated as the inverse of the frequency associated with the maximum value of the wave spectrum (derived in Equation 14 in IEC TS 62600-101)
    • Important for generating spectrums for WEC-Sim (or other modeling programs), where spectrums are defined by T_p and not T_e
    • Can also be used to target resonance bandwidth for WEC design.
  • Zero crossing period (T_z, s)
    • The rate at which a wave transitions from positive to zero to negative (or the reverse) 
    • The period between upward or downward crossing zero or mean water level
      • Inverse of the average number of times the ocean level moves up across the mean water level per second (Wave Energy Potential of Peninsular Malaysia, ARPN Journal of Engineering and Applied Sciences (2010))
    • Statistical calculation from wave spectrum
    • “NT- Important to consider if trying to estimate fatigue damage as the number of counts in the rain flow counter will be tied to this value that energy or peak period.”
• Wave direction (q)
  • Mean wave direction (q_m, deg)
    • The mean of all induvial wave directions in a time series representing a certain sea state
    • Statistical calculation from wave spectrum
    • Used as a design parameter for mooring system design and orientation of unidirectional WECs
    • Reference: IEC TS 62600-101.
  • Maximum energy direction (J_θ,_max, deg)
    • Direction from which the most wave energy is traveling
    • Omnidirectional wave power resolved to a direction
    • Derived in Equation 17 in IEC TS 62600-101
    • Especially important if WEC has a particular orientation
    • Reference: IEC TS 62600-101.
• Wave height (H_m): Mean and extreme values used to characterize operations and maintenance, survival risk, and design loads for normal operations and extreme design load cases
  • Significant wave height (H_s, m, see above)
  • Maximum wave height (H_max, m)
    • Maximum wave height of the wave spectrum
  • Max H_s for different return periods (1–year, 10–year, 50–year) (H_s(n), m)
    
    • H_s(1), H_s(10), H_s(50)
    • The extreme significant wave height for 1–, 10–, and 50–year return periods
    • Calculation: Apply extreme value distribution model to selected significant wave heights from measured or modeled time series
    • Extreme wave heights, ranging from smaller, more frequently occurring wave heights to larger, less frequently occurring ones, characterize extreme wave loads in the IEC design standards for marine energy systems
    • Reference: Global Atlas of Extreme Significant Wave Heights and Relative Risk Ratios, Renewable Energy (2023).
• Relative risk ratio (unitless)
  
  • Hs(50)Hs(mean)
  • Proposed as a useful metric for characterizing risk/cost of wave project to opportunity generate power/revenue (Neary and Ahn 2023)
  • Some anecdotal evidence that is a useful metric for identifying coastal erosion risk for Puerto Rico (Canal-Silander, per. comm. 2020)
  • Reference: (Neary and Ahn 2023).

Wave energy researchers, technology design engineers, and project development engineers might be interested in learning the following terms during the project or technology design stage.

• Directional wave spectrum
• Spectral width (∈_θ)
  • Relative spreading of energy across frequencies in a wave spectrum
  • Derived in Equation 16 in IEC TS 62600-101
  • The greater the spectral width, the greater the range the WEC has to operate in; it can be more reliant on an advanced control system (connected to bandwidth)
    • WECs are designed to operate in particular wave regimes (because resonant devices operate more efficiently in resonance)
  • Reference: IEC 62600-101.
• Directionality coefficient (d_θ)
  • Fraction of total wave energy traveling in the direction of the maximum wave power
  • Measure of the directional spreading of wave power; the ratio of maximum directionally resolved wave power to the omnidirectional wave power
  • Derived in Equation 18 in IEC TS 62600-101
  • Ranges from 0 to 1: the higher the value, the less directional spreading, which is desirable to minimize mooring design complexity and costs
  • Reference: IEC 62600-101.

Development and Validation of a Regional-Scale High-Resolution Unstructured Model for Wave Energy Resource Characterization Along the U.S. East Coast, Renewable Energy (2019)

Predicting Ocean Waves Along the U.S. East Coast During Energetic Winter Storms: Sensitivity to Whitecapping Parameterizations, Ocean Science (2019)

Allahdadi, M.N.; He, R.; Ahn, S.; Chartrand, C.; Neary, V.S. Development and Calibration of a High-Resolution Model for the Gulf of Mexico, Puerto Rico, and the U.S. Virgin Islands: Implication for Wave Energy Resource Characterization. Ocean Engineering 2021, 235, 109304, https://doi:10.1016/j.oceaneng.2021.109304. 

Ahn, Seongho, V.S. Neary, M.N. Allahdadi, and R. He. 2022. “A Framework for Feasibility-Level Validation of High-Resolution Wave Hindcast Models.” Ocean Engineering 263 (1 November 2022): 112193. https://doi.org/10.1016/j.oceaneng.2022.112193.

Wu, Wei-Cheng, T. Wang, Z. Yang, and G. García-Medina. 2020. "Development and Validation of a High-Resolution Regional Wave Hindcast Model for US West Coast Wave Resource Characterization." Renewable Energy 152 (June 2020): 736–753. https://doi.org/10.1016/j.renene.2020.01.077.

García-Medina, Gabriel, Yang, Z., Wu, W.-C., and Wang T. 2021. “Wave Resource Characterization at Regional and Nearshore Scales for the U.S. Alaska Coast Based on a 32-Year High-Resolution Hindcast.” Renewable Energy 170 (June 2021): 595–612. https://doi.org/10.1016/j.renene.2021.02.005.

Li, Ning, G. García-Medina, K. Fai Cheung, and Z. Yang. 2021. “Wave Energy Resources Assessment for the Multi-Modal Sea State of Hawaii.” Renewable Energy 174 (August 2021): 1036–1055. https://doi.org/10.1016/j.renene.2021.03.116.

García Medina, Gabriel, Zhaoqing Yang, Ning Li, Kwok Fai Cheung, and Ellinor Luto-McMoore. 2022. “Wave Climate and Energy Resources in American Samoa From a 42-Year High-Resolution Hindcast.” Renewable Energy.

García Medina, Gabriel, Z. Yang, N. Li, K. Fai Cheung, H. Wang, and F.M. Ticona Rollano. 2021. High-Resolution Regional Wave Hindcast for U.S. Pacific Island Territories. Richland, WA: Pacific Northwest National Laboratory. PNNL-31208. https://doi.org/10.2172/1824210.